HunyuanVideo-1.5 (t2v) is a lightweight 8.3B parameter text-to-video model that generates high-quality videos with top-tier visual quality and motion coherence. Optimized for fast inference on consumer-grade GPUs. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
待機中
$0.11回あたり·~10 / $1
Macro cinematography. A tiny, 3-inch tall robot made of scrap metal and old watch parts is hiking across a wooden kitchen table like it is a mountain range. It struggles to climb over a giant ceramic coffee mug. Suddenly, a giant house cat's yellow eye appears in the blurred background, blinking and staring at the robot. The robot freezes and its lens-eye zooms in. whimsical, detailed textures, depth of field.
Commercial slow-motion shot. Extreme close-up of fresh milk being poured into a clear glass of dark coffee. The liquids swirl and mix, creating intricate marble patterns. Coffee droplets splash upwards in slow motion. Warm morning sunlight backlighting the glass. Crystal clear focus, 8k resolution, high-speed photography aesthetic.
Cinematic close-up. An astronaut in a weathered, futuristic space suit is kneeling on a desolate, red dusty planet surface. The camera reflects off his golden visor. He reaches out a gloved hand, trembling slightly, to touch a single, glowing white flower growing out of a crack in the rock. The contrast between the dead environment and the delicate life is striking. Dust swirls around him. 4k, emotional sci-fi atmosphere.
Surreal dreamscape. A massive, translucent blue humpback whale is swimming slowly and gracefully through the thick white clouds above a modern city skyline at sunset. Golden sunlight passes through the whale's fins. The camera pans to follow the whale's movement. Below, the city lights are just starting to turn on. Magical realism, ethereal, breathtaking scale, Studio Ghibli vibe but photorealistic.
High-speed automotive commercial. A silver sports car is driving aggressively through a well-lit tunnel. The camera is mounted low on the front bumper (POV), rushing forward. The tunnel lights above streak past as continuous lines of light due to the extreme speed. Reflections on the car hood change rapidly. The sense of speed is intense. 4k, hyper-realistic.
HunyuanVideo-1.5 is Tencent’s lightweight text-to-video generation model that delivers state-of-the-art visual quality and motion coherence with only 8.3B parameters. It is designed to be both powerful and efficient, making high-quality video generation accessible for everyday creators and production workflows on WaveSpeedAI.
| Resolution | Price per second |
|---|---|
| 480p | $0.02 / s |
| 720p | $0.04 / s |
After generating your base video with HunyuanVideo-1.5, you can use WaveSpeedAI’s dedicated video super-resolution models to enhance clarity and sharpness:
Generate efficiently at 480p or 720p, then upscale to higher resolutions for a better final viewing experience.
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/hunyuan-video-1.5/text-to-video with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Hunyuan Video 1.5 Text To Video below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": "1280*720",
"duration": 5,
"seed": -1
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/hunyuan-video-1.5/text-to-video" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d "$REQUEST_BODY")
TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
printf 'Submission response did not contain a prediction id
' >&2
exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi
# 2. Poll until the prediction finishes.
while true; do
RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
-H "Authorization: Bearer $WAVESPEED_API_KEY")
RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
case "$STATUS" in
completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
created|processing) sleep 2 ;;
*) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
esac
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/wavespeed-ai/hunyuan-video-1.5/text-to-video";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');
async function requestJson(url, options = {}) {
const response = await fetch(url, options);
if (!response.ok) throw new Error(await response.text());
return response.json();
}
// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
method: "POST",
headers: {
"Authorization": `Bearer ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": "1280*720",
"duration": 5,
"seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;
// 2. Poll until the prediction finishes.
while (true) {
const resultBody = await requestJson(resultUrl, {
headers: { "Authorization": `Bearer ${apiKey}` },
});
const result = resultBody.data ?? resultBody;
if (result.status === "completed") {
console.log(result.outputs);
break;
}
if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
await new Promise(resolve => setTimeout(resolve, 2000));
}import json
import os
import time
from urllib.request import Request, urlopen
api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": "1280*720",
"duration": 5,
"seed": -1
}
def request_json(url, data=None):
request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
with urlopen(request) as response:
return json.load(response)
# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/wavespeed-ai/hunyuan-video-1.5/text-to-video", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"
# 2. Poll until the prediction finishes.
while True:
result_body = request_json(result_url)
result = result_body.get("data", result_body)
status = result.get("status")
if status == "completed":
print(result.get("outputs", []))
break
if status in {"failed", "cancelled", "timeout"}:
raise RuntimeError(result)
if status not in {"created", "processing"}:
raise RuntimeError(f"Unexpected status: {status}")
time.sleep(2)Hunyuan Video 1.5 Text To Video is a WaveSpeedAI model for video generation, exposed as a REST API on WaveSpeedAI. HunyuanVideo-1.5 (t2v) is a lightweight 8.3B parameter text-to-video model that generates high-quality videos with top-tier visual quality and motion coherence. Optimized for fast inference on consumer-grade GPUs. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.
POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID. Poll the result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/hunyuan-video-1.5-text-to-video.
Hunyuan Video 1.5 Text To Video starts at $0.10 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.
Key inputs: `prompt`, `duration`, `size`, `seed`, `negative_prompt`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/wavespeed-ai/hunyuan-video-1.5-text-to-video.
Median end-to-end generation time on WaveSpeedAI is around 194 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.
Commercial usage rights depend on the model's license, set by its provider (WaveSpeedAI). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.